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docs: improve introductory tutorial #2087

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docs: improve introductory tutorial #2087

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12 changes: 6 additions & 6 deletions docs/tutorials/intro_tutorial.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Dependencies\n",
"### Import Dependencies\n",
"This includes importing of dependencies needed for the tutorial."
]
},
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"\n",
"**Model-specific information:** A new class is named `RandomActivationByAgent` is created which extends `mesa.time.RandomActivation` creating a subclass of the `RandomActivation` class from Mesa. This class activates all the agents once per step, in random order. Every agent is expected to have a ``step`` method. The step method is the action the agent takes when it is activated by the model schedule. We add an agent to the schedule using the `add` method; when we call the schedule's `step` method, the model shuffles the order of the agents, then activates and executes each agent's ```step``` method. The scheduler is then added to the model.\n",
"\n",
"**Code implementation:** The technical details about the timer object can be found in the [mesa repo](https://github.com/projectmesa/mesa/blob/main/mesa/time.py). Mesa offers a few different built-in scheduler classes, with a common interface. That makes it easy to change the activation regime a given model uses, and see whether it changes the model behavior. The details pertaining to the scheduler interface can be located the same [mesa repo](https://github.com/projectmesa/mesa/blob/main/mesa/time.py).\n",
"**Code implementation:** The technical details about the timer object can be found in the [mesa repo](https://github.com/projectmesa/mesa/blob/main/mesa/time.py). Mesa offers a few different built-in scheduler classes, with a common interface. That makes it easy to change the activation regime a given model uses, and see whether it changes the model behavior. The details pertaining to the scheduler interface can be located in the same [mesa repo](https://github.com/projectmesa/mesa/blob/main/mesa/time.py).\n",
"\n",
"With that in mind, the `MoneyAgent` code is modified below to visually show when a new agent is created. The MoneyModel code is modified by adding the RandomActivation method to the model. with the scheduler added looks like this:"
]
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"\n",
"Mesa has two main types of grids: `SingleGrid` and `MultiGrid`*. `SingleGrid` enforces at most one agent per cell; `MultiGrid` allows multiple agents to be in the same cell. Since we want agents to be able to share a cell, we use `MultiGrid`.\n",
"\n",
"*However there are more types of space to include `HexGrid`, `NetworkGrid`, and the previously mentioned `ContinuousSpace`. Similar to `mesa.time` context is retained with `mesa.space.[enter class]`. You can see the different classes as [mesa.space](https://github.com/projectmesa/mesa/blob/main/mesa/space.py) "
"*However there are more types of space to include `HexGrid`, `NetworkGrid`, and the previously mentioned `ContinuousSpace`. Similar to `mesa.time` context is retained with `mesa.space.[enter class]`. You can inspect the different classes at [mesa.space](https://github.com/projectmesa/mesa/blob/main/mesa/space.py)."
]
},
{
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"source": [
"At every step of the model, the datacollector will collect and store the model-level current Gini coefficient, as well as each agent's wealth, associating each with the current step.\n",
"\n",
"We run the model just as we did above. Now is when an interactive session, especially via a Notebook, comes in handy: the DataCollector can export the data its collected as a pandas\\* DataFrame, for easy interactive analysis. \n",
"We run the model just as we did above. Now is when an interactive session, especially via a Notebook, comes in handy: the DataCollector can export the data its collected as a pandas* DataFrame, for easy interactive analysis. \n",
"\n",
"\\*If you are new to Python, please be aware that pandas is already installed as a dependency of Mesa and that [pandas](https://pandas.pydata.org/docs/) is a \"fast, powerful, flexible and easy to use open source data analysis and manipulation tool\". pandas is great resource to help analyze the data collected in your models "
"*If you are new to Python, please be aware that pandas is already installed as a dependency of Mesa and that [pandas](https://pandas.pydata.org/docs/) is a \"fast, powerful, flexible and easy to use open source data analysis and manipulation tool\". pandas is great resource to help analyze the data collected in your models."
]
},
{
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"cell_type": "markdown",
"source": [
"### Analyzing model reporters: Comparing 5 scenarios\n",
"Other insight might be gathered when we compare the Gini coefficient of different scenarios. For example, we can compare the Gini coefficient of a population with 25 agents to the Gini coefficient of a population with 400 agents. While doing this, we increase the number of iterations to 25 to get a better estimate of the Gini coefficient for each population size and get usable error estimations."
"Other insights might be gathered when we compare the Gini coefficient of different scenarios. For example, we can compare the Gini coefficient of a population with 25 agents to the Gini coefficient of a population with 400 agents. While doing this, we increase the number of iterations to 25 to get a better estimate of the Gini coefficient for each population size and get usable error estimations."
],
"metadata": {
"collapsed": false
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